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The study develops an object-oriented programming approach using C# to standardize and automate extraction from diverse canine and feline report formats produced by multiple providers. Regular-expression parsers, text cleaning, and curated data dictionaries capture diagnosis, topography, grade, and metastasis, while coordinate mapping supports spatial analysis and diagnosis prioritization. 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Soares Magalhaes 1, 11  \nAbstract  \nCompanion animal cancer diagnostic reports are text-based documents containing essential information on tumor classification and diagnosis. Establishing an animal cancer registry requires integrating and extracting structured data from diverse report formats across multiple providers. This study presents the development of an object-oriented programming approach to standardize and automate cancer data collection for canine and feline patients, enabling the creation of the Australian Companion Animal Registry of Cancers (ACARCinom); Australia’s first national registry of cat and dog cancers. An object-oriented programming approach was developed using the C\\# language for data processing, tested on sample data from 6 data providers. The initial programming phase focused on designing a parser that identified report sections using regular expressions based on standardized headings. The text was then cleaned to remove unnecessary formatting and HTML tags. Data dictionaries containing preferred terms and synonyms were used to extract key information such as diagnosis, topography, grade, and metastasis, improving consistency and accuracy. A coordinate map of extracted terms was generated to analyze spatial relationships within the report, allowing prioritization of diagnoses. The system also logged parsing decisions and potential issues for expert review. Markup using HTML tags enabled clear visualization of parsed content within the original reports. Extracted data and patient metadata were stored in an intermediary database table, allowing veterinary pathology experts to review and refine entries before final import. This automated solution streamlines data extraction and standardization from diverse sources, enabling the efficient analysis of cancer records and enhancing research and surveillance capacity in veterinary oncology.  \nKeywords  \ncancer, cancer registry, cat, data, database, dog  \nCancer data and tumor registries provide a structured and systematic way to collect, store, and analyze information on cancer cases across a defined population.6 These registries offer critical insights into the incidence, prevalence, and trends of different types of cancer, allowing healthcare professionalsand researchers to identify patterns, risk factors, and potential causes.23  \nAs cancer remains a leading cause of illness and death among companion animals, there is a need for comprehensive data to inform clinical practice, guide research, and provide valuable epidemiological insights. Unlike human oncology, where population-based tumor registries are well-established,34 veterinary cancer registries have traditionally been limited in scope, often relying on unstructured hospital- or pathologybased data.7,16,33 The development of more robust and sustainable veterinary cancer registries is essential to accurately monitor cancer incidence, identify risk factors, and evaluate treatment outcomes in animals. In many cases, the structure  \nand content of veterinary pathology and clinical reports are not standardized and may exist in a variety of formats, including handwritten notes, free-text entries within electronic medical  \n1 The University of Queensland, Gatton, QLD, Australia 2Identic Pty Ltd, Brisbane, QLD, Austral","cbCaik0ONm7lI9gv","https://ap.wps.com/l/cbCaik0ONm7lI9gv","pdf",919992,11,"English","# Abstract\n# Keywords","[{\"question\":\"Why are companion animal cancer diagnostic reports difficult to use for registries?\",\"answer\":\"They are primarily text-based and come in many formats across providers and institutions. Variations in headings, wording, abbreviations, and structure make reliable extraction challenging.\"},{\"question\":\"What programming approach and tools were used to implement the protocol?\",\"answer\":\"The protocol uses an object-oriented programming approach developed in C#. Regular expressions identify report sections, followed by text cleaning to remove unnecessary formatting and HTML tags.\"},{\"question\":\"How does the system improve consistency and accuracy of extracted cancer information?\",\"answer\":\"Curated data dictionaries with preferred terms and synonyms extract key fields such as diagnosis, topography, grade, and metastasis consistently. The workflow logs parsing decisions for expert review, and results are stored in an intermediary database for refinement.\"}]","Development and implementation of a data parsing protocol for companion animal cancer data | PDF",1790058097,28]